# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""Multi-Document Dataset."""


import json

import datasets
from datasets import set_caching_enabled


set_caching_enabled(False)

_CITATION = """
@article{lu2020multi,
  title={Multi-Document: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
  author={Arka Das, India},
  journal={arXiv preprint arXiv:2010.14235},
  year={2022}
}
"""

_DESCRIPTION = """
Multi-Document, a large-scale multi-document summarization dataset created from scientific articles. Multi-Document introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references.
"""

_URL_TRAIN = "https://github.com/arka0821/multi_document_summarization/raw/master/data/train.json.gz"
_URL_TEST = "https://github.com/arka0821/multi_document_summarization/raw/master/data/test.json.gz"
_URL_VAL = "https://github.com/arka0821/multi_document_summarization/raw/master/data/val.json.gz"


class MultiDocumentSum(datasets.GeneratorBasedBuilder):
    """ "Multi-Document Dataset."""

    VERSION = datasets.Version("1.1.0")
    def _info(selif):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "docs": datasets.Sequence(
                        {
                            "id": datasets.Value("string"),
                            "text": datasets.Value("string")
                        },
                    ),
                    "summary": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/arka0821/multi_document_summarization",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        train_path = dl_manager.download_and_extract(_URL_TRAIN)
        test_path = dl_manager.download_and_extract(_URL_TEST)
        val_path = dl_manager.download_and_extract(_URL_VAL)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"path": train_path},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"path": test_path},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"path": val_path},
            ),
        ]
    def _generate_examples(self, path=None):
        """Yields examples."""
        with open(path, encoding="utf-8") as f:
            data = json.load(f)
            f.close()
        for idx, el in enumerate(data):            
            ids = [id["id"] for id in el["docs"]]
            texts = [text["text"] for text in el["docs"]]
            tmp = {"id": ids, "text": texts}
            d = el.copy()
            d["docs"] = tmp
            yield idx, d